Systems and methods for intelligent promotion design with promotion scoring

ABSTRACT

Systems and methods for scoring promotions are provided. A set of training offers are received, which include combinations of variable values. These combinations of variable values are converted into a vector value. The offers are paired and the vectors subtracted from one another, resulting in a pair vector. Metrics for the success of offers is collected, and are subtracted from one another for the paired offers to generate a raw score. This raw score is then normalized using the pair vector. The normalized scores are utilized to generate a model for the impact any variable value has on offer success, which may then be applied, using linear regression, to new offers to generate an expected level of success. The new scored offers are ranked and the top-ranked offers are selected for inclusion in a promotional campaign.

CROSS REFERENCE TO RELATED APPLICATIONS

This continuation-in-part application claims the benefit of U.S. Thiscontinuation-in-part application claims the benefit of U.S. applicationentitled “Architecture and Methods for Promotion Optimization,” U.S.application Ser. No. 14/209,851, filed in the USPTO on Mar. 13, 2014, byinventor Moran, which claims priority under 35 U.S.C. 119(e) to acommonly owned U.S. provisional patent application entitled“Architecture and Methods for Promotion Optimization,” U.S. ApplicationNo. 61/780,630, filed in the USPTO on Mar. 13, 2013, by inventor Moran,all of which are incorporated herein by reference.

The present invention is related to the following pending applications,all of which are incorporated herein by reference:

Commonly owned U.S. application Ser. No. 14/231,426, filed on Mar. 31,2014, entitled “Adaptive Experimentation and Optimization in AutomatedPromotional Testing,” by Moran et al.

Commonly owned U.S. application Ser. No. 14/231,432, filed on Mar. 31,2014, entitled “Automated and Optimal Promotional Experimental TestDesigns Incorporating Constraints,” by Moran et al.

Commonly owned U.S. application Ser. No. 14/231,440, filed on Mar. 31,2014, entitled “Automatic Offer Generation Using Concept GeneratorApparatus and Methods Therefor,” by Moran et al.

Commonly owned U.S. application Ser. No. 14/231,442, filed on Mar. 31,2014, entitled “Automated Event Correlation to Improve PromotionalTesting,” by Moran et al.

Commonly owned U.S. application Ser. No. 14/231,460, filed on Mar. 31,2014, entitled “Automated Promotion Forecasting and Methods Therefor,”by Moran et al.

Commonly owned U.S. application Ser. No. 14/231,555, filed on Mar. 31,2014, entitled “Automated Behavioral Economics Patterns in PromotionTesting and Methods Therefor,” by Moran et al.

BACKGROUND

The present invention relates generally to promotion optimizationmethods and apparatus therefor. More particularly, the present inventionrelates to computer-implemented methods and computer-implementedapparatus for the generation of a batch of promotions utilizingintelligent design criteria to maximize promotion experimentation.

Promotion refers to various practices designed to increase sales of aparticular product or services and/or the profit associated with suchsales. Generally speaking, the public often associates promotion withthe sale of consumer goods and services, including consumer packagedgoods (e.g., food, home and personal care), consumer durables (e.g.,consumer appliances, consumer electronics, automotive leasing), consumerservices (e.g., retail financial services, health care, insurance, homerepair, beauty and personal care), and travel and hospitality (e.g.,hotels, airline flights, and restaurants). Promotion is particularlyheavily involved in the sale of consumer packaged goods (i.e., consumergoods packaged for sale to an end consumer). However, promotion occursin almost any industry that offers goods or services to a buyer (whetherthe buyer is an end consumer or an intermediate entity between theproducer and the end consumer).

The term promotion may refer to, for example, providing discounts (usingfor example a physical or electronic coupon or code) designed to, forexample, promote the sales volume of a particular product or service.One aspect of promotion may also refer to the bundling of goods orservices to create a more desirable selling unit such that sales volumemay be improved. Another aspect of promotion may also refer to themerchandising design (with respect to looks, weight, design, color,etc.) or displaying of a particular product with a view to increasingits sales volume. It includes calls to action or marketing claims usedin-store, on marketing collaterals, or on the package to drive demand.Promotions may be composed of all or some of the following: price basedclaims, secondary displays or aisle end-caps in a retail store, shelfsignage, temporary packaging, placement in a retailercircular/flyer/coupon book, a colored price tag, advertising claims, orother special incentives intended to drive consideration and purchasebehavior. These examples are meant to be illustrative and not limiting.

In discussing various embodiments of the present invention, the sale ofconsumer packaged goods (hereinafter “CPG”) is employed to facilitatediscussion and ease of understanding. It should be kept in mind,however, that the promotion optimization methods and apparatusesdiscussed herein may apply to any industry in which promotion has beenemployed in the past or may be employed in the future.

Further, price discount is employed as an example to explain thepromotion methods and apparatuses herein. It should be understood,however, that promotion optimization may be employed to manipulatefactors other than price discount in order to influence the salesvolume. An example of such other factors may include the call to actionon a display or on the packaging, the size of the CPG item, the mannerin which the item is displayed or promoted or advertised either in thestore or in media, etc.

Generally speaking, it has been estimated that, on average, 17% of therevenue in the consumer packaged goods (CPG) industry is spent to fundvarious types of promotions, including discounts, designed to enticeconsumers to try and/or to purchase the packaged goods. In a typicalexample, the retailer (such as a grocery store) may offer a discountonline or via a print circular to consumers. The promotion may bespecifically targeted to an individual consumer (based on, for example,that consumer's demographics or past buying behavior). The discount mayalternatively be broadly offered to the general public. Examples ofpromotions offered to general public include for example, a printed orelectronic redeemable discount (e.g., coupon or code) for a specific CPGitem. Another promotion example may include, for example, generaladvertising of the reduced price of a CPG item in a particulargeographic area. Another promotion example may include in-store markingdown of a particular CPG item only for a loyalty card user base.

In an example, if the consumer redeems the coupon or electronic code,the consumer is entitled to a reduced price for the CPG item. Therevenue loss to the retailer due to the redeemed discount may bereimbursed, wholly or partly, by the manufacturer of the CPG item in aseparate transaction.

Because promotion is expensive (in terms of, for example, the effort toconduct a promotion campaign and/or the per-unit revenue loss to theretailer/manufacturer when the consumer decides to take advantage of thediscount), efforts are continually made to minimize promotion cost whilemaximizing the return on promotion dollars investment. This effort isknown in the industry as promotion optimization.

For example, a typical promotion optimization method may involveexamining the sales volume of a particular CPG item over time (e.g.,weeks). The sales volume may be represented by a demand curve as afunction of time, for example. A demand curve lift (excess overbaseline) or dip (below baseline) for a particular time period would beexamined to understand why the sales volume for that CPG item increasesor decreases during such time period.

FIG. 1 shows an example demand curve 102 for Brand X cookies over someperiod of time. Two lifts 110 and 114 and one dip 112 in demand curve102 are shown in the example of FIG. 1. Lift 110 shows that the demandfor Brand X cookies exceeds the baseline at least during week 2. Byexamining the promotion effort that was undertaken at that time (e.g.,in the vicinity of weeks 1-4 or week 2) for Brand X cookies, marketershave in the past attempted to judge the effectiveness of the promotioneffort on the sales volume. If the sales volume is deemed to have beencaused by the promotion effort and delivers certain financialperformance metrics, that promotion effort is deemed to have beensuccessful and may be replicated in the future in an attempt to increasethe sales volume. On the other hand, dip 112 is examined in an attemptto understand why the demand falls off during that time (e.g., weeks 3and 4 in FIG. 1). If the decrease in demand was due to the promotion inweek 2 (also known as consumer pantry loading or retailerforward-buying, depending on whether the sales volume shown reflects thesales to consumers or the sales to retailers), this decrease in weeks 3and 4 should be counted against the effectiveness of week 2.

One problem with the approach employed in the prior art has been thefact that the prior art approach is a backward-looking approach based onaggregate historical data. In other words, the prior art approachattempts to ascertain the nature and extent of the relationship betweenthe promotion and the sales volume by examining aggregate data collectedin the past. The use of historical data, while having some disadvantages(which are discussed later herein below), is not necessarily a problem.However, when such data is in the form of aggregate data (such as insimple terms of sales volume of Brand X cookies versus time for aparticular store or geographic area), it is impossible to extract fromsuch aggregate historical data all of the other factors that may morelogically explain a particular lift or dip in the demand curve.

To elaborate, current promotion optimization approaches tend to evaluatesales lifts or dips as a function of four main factors: discount depth(e.g., how much was the discount on the CPG item), discount duration(e.g., how long did the promotion campaign last), timing (e.g., whetherthere was any special holidays or event or weather involved), andpromotion type (e.g., whether the promotion was a price discount only,whether Brand X cookies were displayed/not displayed prominently,whether Brand X cookies were features/not featured in the promotionliterature).

However, there may exist other factors that contribute to the sales liftor dip, and such factors are often not discoverable by examining, in abackward-looking manner, the historical aggregate sales volume data forBrand X cookies. This is because there is not enough information in theaggregate sales volume data to enable the extraction of informationpertaining to unanticipated or seemingly unrelated events that may havehappened during the sales lifts and dips and may have actuallycontributed to the sales lifts and dips.

Suppose, for example, that there was a discount promotion for Brand Xcookies during the time when lift 110 in the demand curve 102 happens.However, during the same time, there was a breakdown in the distributionchain of Brand Y cookies, a competitor's cookies brand which manyconsumers view to be an equivalent substitute for Brand X cookies. WithBrand Y cookies being in short supply in the store, many consumersbought Brand X instead for convenience sake. Aggregate historical salesvolume data for Brand X cookies, when examined after the fact inisolation by Brand X marketing department thousands of miles away, wouldnot uncover that fact. As a result, Brand X marketers may make themistaken assumption that the costly promotion effort of Brand X cookieswas solely responsible for the sales lift and should be continued,despite the fact that it was an unrelated event that contributed to mostof the lift in the sales volume of Brand X cookies.

As another example, suppose, for example, that milk produced by aparticular unrelated vendor was heavily promoted in the same grocerystore or in a different grocery store nearby during the week that BrandX cookies experienced the sales lift 110. The milk may have beenhighlighted in the weekly circular, placed in a highly visible locationin the store and/or a milk industry expert may have been present in thestore to push buyers to purchase milk, for example. Many consumers endedup buying milk because of this effort whereas some of most of thoseconsumers who bought during the milk promotion may have waited anotherweek or so until they finished consuming the milk they bought in theprevious weeks. Further, many of those milk-buying consumers during thisperiod also purchased cookies out of an ingrained milk-and-cookieshabit. Aggregate historical sales volume data for Brand X cookies wouldnot uncover that fact unless the person analyzing the historicalaggregate sales volume data for Brand X cookies happened to be presentin the store during that week and had the insight to note that milk washeavily promoted that week and also the insight that increased milkbuying may have an influence on the sales volume of Brand X cookies.

Software may try to take these unanticipated events into account butunless every SKU (stock keeping unit) in that store and in stores withincommuting distance and all events, whether seemingly related orunrelated to the sales of Brand X cookies, are modeled, it is impossibleto eliminate data noise from the backward-looking analysis based onaggregate historical sales data.

Even without the presence of unanticipated factors, a marketing personworking for Brand X may be interested in knowing whether the relativelymodest sales lift 114 comes from purchases made by regular Brand Xcookies buyers or by new buyers being enticed by some aspect of thepromotion campaign to buy Brand X cookies for the first time. If Brand Xmarketer can ascertain that most of the lift in sales during thepromotion period that spans lift 114 comes from new consumers of Brand Xcookies, such marketer may be willing to spend more money on the sametype of sales promotion, even to the point of tolerating a negative ROI(return on investment) on his promotion dollars for this particular typeof promotion since the recruitment of new buyers to a brand is deemedmore much valuable to the company in the long run than the temporaryincrease in sales to existing Brand X buyers. Again, aggregatehistorical sales volume data for Brand X cookies, when examined in abackward-looking manner, would not provide such information.

Furthermore, even if all unrelated and related events and factors can bemodeled, the fact that the approach is backward-looking means that thereis no way to validate the hypothesis about the effect an event has onthe sales volume since the event has already occurred in the past. Withrespect to the example involving the effect of milk promotion on Brand Xcookies sales, there is no way to test the theory short of duplicatingthe milk shortage problem again. Even if the milk shortage problem couldbe duplicated again for testing purposes, other conditions have changed,including the fact that most consumers who bought milk during thatperiod would not need to or be in a position to buy milk again in a longtime. Some factors, such as weather, cannot be duplicated, making theoryverification challenging.

Attempts have been made to employ non-aggregate sales data in promotingproducts. For example, some companies may employ a loyalty card program(such as the type commonly used in grocery stores or drug stores) tokeep track of purchases by individual consumers. If an individualconsumer has been buying sugar-free cereal, for example, themanufacturer of a new type of whole grain cereal may wish to offer adiscount to that particular consumer to entice that consumer to try outthe new whole grain cereal based on the theory that people who boughtsugar-free cereal tend to be more health conscious and thus more likelyto purchase whole grain cereal than the general cereal-consuming public.Such individualized discount may take the form of, for example, aredeemable discount such as a coupon or a discount code mailed oremailed to that individual.

Some companies may vary the approach by, for example, ascertaining theitems purchased by the consumer at the point of sale terminal andoffering a redeemable code on the purchase receipt. Irrespective of theapproach taken, the utilization of non-aggregate sales data hastypically resulted in individualized offers, and has not been processedor integrated in any meaningful sense into a promotion optimizationeffort to determine the most cost-efficient, highest-return manner topromote a particular CPG item to the general public.

Attempts have also been made to obtain from the consumers themselvesindications of future buying behavior instead of relying on abackward-looking approach. For example, conjoint studies, one of thestated preference methods, have been attempted in which consumers areasked to state preferences. In an example conjoint study, a consumer maybe approached at the store and asked a series of questions designed touncover the consumer's future shopping behavior when presented withdifferent promotions. Questions may be asked include, for example, “doyou prefer Brand X or Brand Y” or “do you spend less than $100 or morethan $100 weekly on grocery” or “do you prefer chocolate cookies oroatmeal cookies” or “do you prefer a 50-cent-off coupon or a 2-for-1deal on cookies”. The consumer may state his preference on each of thequestions posed (thus making this study a conjoint study on statedpreference).

However, such conjoint studies have proven to be an expensive way toobtain non-historical data. If the conjoint studies are presented via acomputer, most users may ignore the questions and/or refuse toparticipate. If human field personnel are employed to talk to individualconsumers to conduct the conjoint study, the cost of such studies tendsto be quite high due to salary cost of the human field personnel and maymake the extensive use of such conjoint studies impractical.

Further and more importantly, it has been known that conjoint studiesare somewhat unreliable in gauging actual purchasing behavior byconsumers in the future. An individual may state out of guilt and theknowledge that he needs to lose weight that he will not purchase anycookies in the next six months, irrespective of discounts. In actuality,that individual may pick up a package of cookies every week if suchpackage is carried in a certain small size that is less guilt-inducingand/or if the package of cookies is prominently displayed next to themilk refrigerator and/or if a 10% off discount coupon is available. If apromotion effort is based on such flawed stated preference data,discounts may be inefficiently deployed in the future, costing themanufacturer more money than necessary for the promotion.

Finally, none of the approaches track the long-term impact of apromotion's effect on brand equity for an individual's buying behaviorover time. Some promotions, even if deemed a success by traditionalshort-term measures, could have damaging long-term consequences.Increased price-based discounting, for example, can lead to consumersincreasing the weight of price in determining their purchase decisions,making consumers more deal-prone and reluctant to buy at full price,leading to less loyalty to brands and retail outlets.

Previous disclosures by the applicants have focused upon the ability togenerate and administer a plurality of test promotions across consumersegments in a rapid manner in order to overcome the foregoing issues ina manner that results in cost-effective, high-return, and timelypromotions to the general public. However, there are still remainingissues regarding how to best generate the initial promotions.Previously, ad managers have often relied upon intuition and historicalactivity to generate the promotions presented to the users. Suchsystems, even when able to rapidly generate and deploy numerousadvertising campaigns often result in missed opportunity since theinitial design constraints put forth by a user is less than ideal.

It is therefore apparent that an urgent need exists for systems andmethods that enable a user to generate advertisement designs that mosteffectively explore the experimental space of a possible promotion inorder to efficiently hone in on potent general promotions.

SUMMARY

To achieve the foregoing and in accordance with the present invention,systems and methods for the generation of intelligent promotionaldesigns is provided.

In some embodiments, methods and systems for scoring the promotions areprovided. In these methods and systems a set of training offers areinitially received. The training offers have a set of variables, eachwith a set of possible values. Each offer may thus be defined by itscombination or variable values. These combinations of variable valuesmay be converted into a vector value. The offers may then be paired andthe vectors or each offer subtracted from one another. This results in apair vector.

Metrics for the success of offers is collected from a retailer's pointof sales system (or other suitable metric, such as share rate, kliprates, view rates, impression measures, online redemption, saving theoffer, liking the offer, etc.). The success metrics may be consolidatedinto a weighted average in some instances. For example redemption may beafforded a larger weight than saving, which may be larger than viewing,for example.

The success metrics for the paired offers are subtracted from oneanother to generate a raw score. This raw score is then normalized usingthe pair vector. The normalized scores are utilized to generate a modelfor the impact any variable value has on offer success, which may thenbe applied, using linear regression, to new offers to generate anexpected level of success. This may involve generating an estimatedscore and t-value for the new offers, either of which may be utilized asthe score for the new offer, in some embodiments.

The model may be generated through machine learning, and may includeeither a decision tree or a neural network. The model type can bedependent upon data scale. For example, for the data of a singleretailer, a decision tree may be utilized, whereas for multipleretailers' data a neural network may be preferred.

Lastly, the new scored offers may be ranked and the top-ranked offers(typically between 4-10 offers, or top 10-30% of offers) may be selectedfor inclusion in a promotional campaign.

Note that the various features of the present invention described abovemay be practiced alone or in combination. These and other features ofthe present invention will be described in more detail below in thedetailed description of the invention and in conjunction with thefollowing figures.

BRIEF DESCRIPTION OF THE DRAWINGS

In order that the present invention may be more clearly ascertained,some embodiments will now be described, by way of example, withreference to the accompanying drawings, in which:

FIG. 1 shows an example demand curve 102 for Brand X cookies over someperiod of time;

FIG. 2A shows, in accordance with an embodiment of the invention, aconceptual drawing of the forward-looking promotion optimization method;

FIG. 2B shows, in accordance with an embodiment of the invention, thesteps for generating a general public promotion;

FIG. 3A shows in greater detail, in accordance with an embodiment of theinvention, the administering step 206 of FIG. 2 from the user'sperspective;

FIG. 3B shows in greater detail, in accordance with an embodiment of theinvention, the administering step 206 of FIG. 2 from the forward-lookingpromotion optimization system perspective;

FIG. 4 shows various example segmentation criteria that may be employedto generate the purposefully segmented subpopulations;

FIG. 5 shows various example methods for communicating the testpromotions to individuals of the segmented subpopulations being tested;

FIG. 6 shows, in accordance with some embodiments, various examplepromotion-significant responses;

FIG. 7 shows, in accordance with some embodiments, various example testpromotion variables affecting various aspects of a typical testpromotion;

FIG. 8 shows, in accordance with some embodiments, a generalhardware/network view of a forward-looking promotion optimizationsystem;

FIG. 9 shows, in accordance with some embodiments, a block diagram of anintelligent promotional design architecture;

FIG. 10 shows, in accordance with some embodiments, an example blockdiagram of the intelligent offer design system;

FIGS. 11A-C show, in accordance with some embodiments, example tablesillustrating possible variable values with and without appliedheuristics;

FIGS. 12A-B show, in accordance with some embodiments, the selection oftop offers based upon performance modeling from the variable valuetables that have been subjected to heuristic refinement;

FIGS. 13-15 show, in accordance with some embodiments, example tables ofthe scoring of the selected top promotions;

FIG. 16 shows, in accordance with some embodiments, the variable valuetable where the remaining X percentage of the offers are selected forpromotional experimentation;

FIGS. 17A and 17B show, in accordance with some embodiments, examples of“optimal” promotion selections versus “non-optimal” promotionselections;

FIG. 18 shows, in accordance with some embodiments, a flowchart of anexample method for the generation and application of intelligentpromotional designs;

FIG. 19 shows, in accordance with some embodiments, a flowchart of anexample method for the scoring of the selected top promotions; and

FIGS. 20A and 20B are example computer systems capable of implementingthe system for design matrix generation and recommendation overlay.

DETAILED DESCRIPTION

The present invention will now be described in detail with reference toseveral embodiments thereof as illustrated in the accompanying drawings.In the following description, numerous specific details are set forth inorder to provide a thorough understanding of embodiments of the presentinvention. It will be apparent, however, to one skilled in the art, thatembodiments may be practiced without some or all of these specificdetails. In other instances, well known process steps and/or structureshave not been described in detail in order to not unnecessarily obscurethe present invention. The features and advantages of embodiments may bebetter understood with reference to the drawings and discussions thatfollow.

Aspects, features and advantages of exemplary embodiments of the presentinvention will become better understood with regard to the followingdescription in connection with the accompanying drawing(s). It should beapparent to those skilled in the art that the described embodiments ofthe present invention provided herein are illustrative only and notlimiting, having been presented by way of example only. All featuresdisclosed in this description may be replaced by alternative featuresserving the same or similar purpose, unless expressly stated otherwise.Therefore, numerous other embodiments of the modifications thereof arecontemplated as falling within the scope of the present invention asdefined herein and equivalents thereto. Hence, use of absolute and/orsequential terms, such as, for example, “will,” “will not,” “shall,”“shall not,” “must,” “must not,” “first,” “initially,” “next,”“subsequently,” “before,” “after,” “lastly,” and “finally,” are notmeant to limit the scope of the present invention as the embodimentsdisclosed herein are merely exemplary.

The present invention relates to the generation of intelligentpromotional designs for most effective experimentation of promotions tomore efficiently identify a highly effective general promotion. Suchsystems and methods assist administrator users to generate and deployadvertising campaigns. While such systems and methods may be utilizedwith any promotional setting system, such intelligent promotional designsystems particularly excel when coupled with systems for optimizingpromotions by administering, in large numbers and iteratively, testpromotions on purposefully segmented subpopulations in advance of ageneral public promotion roll-out. In one or more embodiments, theinventive forward-looking promotion optimization (FL-PO) involvesobtaining actual revealed preferences from individual consumers of thesegmented subpopulations being tested. As such the following disclosurewill focus upon mechanisms of forward looking promotional optimizations,in order to understand the context within which the intelligentpromotional design system excels.

The following description of some embodiments will be provided inrelation to numerous subsections. The use of subsections, with headings,is intended to provide greater clarity and structure to the presentinvention. In no way are the subsections intended to limit or constrainthe disclosure contained therein. Thus, disclosures in any one sectionare intended to apply to all other sections, as is applicable.

I. Forward Looking Promotion Optimization

Within the forward-looking promotion optimization, the revealedpreferences are obtained when the individual consumers respond tospecifically designed actual test promotions. The revealed preferencesare tracked in individual computer-implemented accounts (which may, forexample, be implemented via a record in a centralized database andrendered accessible to the merchant or the consumer via a computernetwork such as the internet) associated with individual consumers. Forexample, when a consumer responds, using his smart phone or web browser,to a test promotion that offers 20% off a particular consumer packagedgoods (CPG) item, that response is tracked in his individualcomputer-implemented account. Such computer-implemented accounts may beimplemented via, for example, a loyalty card program, apps on a smartphone, computerized records accessible via a browser, social media newsfeed, etc.

In one or more embodiments, a plurality of test promotions may bedesigned and tested on a plurality of groups of consumers (the groups ofconsumers are referred to herein as “subpopulations”). The responses bythe consumers are recorded and analyzed, with the analysis resultemployed to generate additional test promotions or to formulate thegeneral population promotion.

As will be discussed later herein, if the consumer actually redeems theoffer, one type of response is recorded and noted in thecomputer-implemented account of that consumer. Even if an action by theconsumer does not involve actually redeeming or actually takingadvantage of the promotional offer right away, an action by thatconsumer may, however, constitute a response that indicates a level ofinterest or lack of interest and may still be useful in revealing theconsumer preference (or lack thereof). For example, if a consumer savesan electronic coupon (offered as part of a test promotion) in hiselectronic coupon folder or forwards that coupon to a friend via anemail or a social website, that action may indicate a certain level ofinterest and may be useful in determining the effectiveness of a giventest promotion. Different types of responses by the consumers may beaccorded different weights, in one or more embodiments.

The groups of consumers involved in promotion testing represent segmentsof the public that have been purposefully segmented in accordance withsegmenting criteria specifically designed for the purpose of testing thetest promotions. As the term is employed herein, a subpopulation isdeemed purposefully segmented when its members are selected based oncriteria other than merely to make up a given number of members in thesubpopulation. Demographics, buying behavior, behavioral economics areexample criteria that may be employed to purposefully segment apopulation into subpopulations for promotion testing. In an example, asegmented population may number in the tens or hundreds or eventhousands of individuals. In contrast, the general public may involvetens of thousands, hundreds of thousands, or millions of potentialcustomers.

By purposefully segmenting the public into small subpopulations forpromotion testing, embodiments of the invention can exert control overvariables such as demographics (e.g., age, income, sex, marriage status,address, etc.), buying behavior (e.g., regular purchaser of Brand Xcookies, consumer of premium food, frequent traveler, etc), weather,shopping habits, life style, and/or any other criteria suitable for usein creating the subpopulations. More importantly, the subpopulations arekept small such that multiple test promotions may be executed ondifferent subpopulations, either simultaneously or at different times,without undue cost or delay in order to obtain data pertaining to thetest promotion response behavior. The low cost/low delay aspect ofcreating and executing test promotions on purposefully segmentedsubpopulations permits, for example, what-if testing, testing instatistically significant numbers of tests, and/or iterative testing toisolate winning features in test promotions.

Generally speaking, each individual test promotion may be designed totest one or more test promotion variables. These test promotionsvariables may relate to, for example, the size, shape, color, manner ofdisplay, manner of discount, manner of publicizing, manner ofdissemination pertaining to the goods/services being promoted.

As a very simple example, one test promotion may involve 12-oz packagesof fancy-cut potato chips with medium salt and a discount of 30% off theregular price. This test promotion may be tested on a purposefullysegmented subpopulation of 35-40 years old professionals in the$30,000-$50,000 annual income range. Another test promotion may involvethe same 30% discount 12-oz packages of fancy-cut potato chips withmedium salt on a different purposefully segmented subpopulation of 35-40years old professionals in the higher $100,000-$150,000 annual incomerange. By controlling all variables except for income range, theresponses of these two test promotions, if repeated in statisticallysignificant numbers, would likely yield fairly accurate informationregarding the relationship between income for 35-40 years oldprofessionals and their actual preference for 12-oz packages of fancycut potato chips with medium salt.

In designing different test promotions, one or more of the testpromotions variables may vary or one or more of the segmenting criteriaemployed to create the purposefully segmented subpopulations may vary.The test promotion responses from individuals in the subpopulations arethen collected and analyzed to ascertain which test promotion or testpromotion variable(s) yields/yield the most desirable response (based onsome predefined success criteria, for example).

Further, the test promotions can also reveal insights regarding whichsubpopulation performs the best or well with respect to test promotionresponses. In this manner, test promotion response analysis providesinsights not only regarding the relative performance of the testpromotion and/or test promotion variable but also insights regardingpopulation segmentation and/or segmentation criteria. In an embodiment,it is contemplated that the segments may be arbitrarily or randomlysegmented into groups and test promotions may be executed against thesearbitrarily segmented groups in order to obtain insights regardingpersonal characteristics that respond well to a particular type ofpromotion.

In an embodiment, the identified test promotion variable(s) that yieldthe most desirable responses may then be employed to formulate a generalpublic promotion (GPP), which may then be offered to the larger public.A general public promotion is different from a test promotion in that ageneral public promotion is a promotion designed to be offered tomembers of the public to increase or maximize sales or profit whereas atest promotion is designed to be targeted to a small group ofindividuals fitting a specific segmentation criteria for the purpose ofpromotion testing. Examples of general public promotions include (butnot limited to) advertisement printed in newspapers, release in publicforums and websites, flyers for general distribution, announcement onradios or television, and/or promotion broadly transmitted or madeavailable to members of the public. The general public promotion maytake the form of a paper or electronic circular that offers the samepromotion to the larger public, for example.

Alternatively or additionally, promotion testing may be iterated overand over with different subpopulations (segmented using the same ordifferent segmenting criteria) and different test promotions (devisedusing the same or different combinations of test promotion variables) inorder to validate one or more the test promotion response analysisresult(s) prior to the formation of the generalized public promotion. Inthis manner, “false positives” may be reduced.

Since a test promotion may involve many test promotion variables,iterative test promotion testing, as mentioned, may help pin-point avariable (i.e., promotion feature) that yields the most desirable testpromotion response to a particular subpopulation or to the generalpublic.

Suppose, for example, that a manufacturer wishes to find out the mosteffective test promotion for packaged potato chips. One test promotionmay reveal that consumers tend to buy a greater quantity of potato chipswhen packaged in brown paper bags versus green paper bags. That“winning” test promotion variable value (i.e., brown paper bagpackaging) may be retested in another set of test promotions usingdifferent combinations of test promotion variables (such as for examplewith different prices, different display options, etc.) on the same ordifferent purposefully segmented subpopulations. The follow-up testpromotions may be iterated multiple times in different test promotionvariable combinations and/or with different test subpopulations tovalidate that there is, for example, a significant consumer preferencefor brown paper bag packaging over other types of packaging for potatochips.

Further, individual “winning” test promotion variable values fromdifferent test promotions may be combined to enhance the efficacy of thegeneral public promotion to be created. For example, if a 2-for-1discount is found to be another winning variable value (e.g., consumerstend to buy a greater quantity of potato chips when offered a 2-for-1discount), that winning test promotion variable value (e.g., theaforementioned 2-for-1 discount) of the winning test promotion variable(e.g., discount depth) may be combined with the brown paper packagingwinning variable value to yield a promotion that involves discounting2-for-1 potato chips in brown paper bag packaging.

The promotion involving discounting 2-for-1 potato chips in brown paperbag packaging may be tested further to validate the hypothesis that sucha combination elicits a more desirable response than the response fromtest promotions using only brown paper bag packaging or from testpromotions using only 2-for-1 discounts. As many of the “winning” testpromotion variable values may be identified and combined in a singlepromotion as desired. At some point, a combination of “winning” testpromotion variables (involving one, two, three, or more “winning” testpromotion variables) may be employed to create the general publicpromotion, in one or more embodiments.

In one or more embodiments, test promotions may be executed iterativelyand/or in a continual fashion on different purposefully segmentedsubpopulations using different combinations of test promotion variablesto continue to obtain insights into consumer actual revealedpreferences, even as those preferences change over time. Note that theconsumer responses that are obtained from the test promotions are actualrevealed preferences instead of stated preferences. In other words, thedata obtained from the test promotions administered in accordance withembodiments of the invention pertains to what individual consumersactually do when presented with the actual promotions. The data istracked and available for analysis and/or verification in individualcomputer-implemented accounts of individual consumers involved in thetest promotions. This revealed preference approach is opposed to astated preference approach, which stated preference data is obtainedwhen the consumer states what he would hypothetically do in response to,for example, a hypothetically posed conjoint test question.

As such, the actual preference test promotion response data obtained inaccordance with embodiments of the present invention is a more reliableindicator of what a general population member may be expected to behavewhen presented with the same or a similar promotion in a general publicpromotion. Accordingly, there is a closer relationship between the testpromotion response behavior (obtained in response to the testpromotions) and the general public response behavior when a generalpublic promotion is generated based on such test promotion responsedata.

Also, the lower face validity of a stated preference test, even if theinsights have statistical relevance, poses a practical challenge; CPGmanufacturers who conduct such tests have to then communicate theinsights to a retailer in order to drive real-world behavior, andconvincing retailers of the validity of these tests after the fact canlead to lower credibility and lower adoption, or “signal loss” as thetop concepts from these tests get re-interpreted by a third party, theretailer, who wasn't involved in the original test design.

It should be pointed out that embodiments of the inventive testpromotion optimization methods and apparatuses disclosed herein operateon a forward-looking basis in that the plurality of test promotions aregenerated and tested on segmented subpopulations in advance of theformulation of a general public promotion. In other words, the analysisresults from executing the plurality of test promotions on differentpurposefully segmented subpopulations are employed to generate futuregeneral public promotions. In this manner, data regarding the “expected”efficacy of the proposed general public promotion is obtained evenbefore the proposed general public promotion is released to the public.This is one key driver in obtaining highly effective general publicpromotions at low cost.

Furthermore, the subpopulations can be generated with highly granularsegmenting criteria, allowing for control of data noise that may arisedue to a number of factors, some of which may be out of the control ofthe manufacturer or the merchant. This is in contrast to the aggregateddata approach of the prior art.

For example, if two different test promotions are executed on twosubpopulations shopping at the same merchant on the same date,variations in the response behavior due to time of day or trafficcondition are essentially eliminated or substantially minimized in theresults (since the time or day or traffic condition would affect the twosubpopulations being tested in substantially the same way).

The test promotions themselves may be formulated to isolate specifictest promotion variables (such as the aforementioned potato chip brownpaper packaging or the 16-oz size packaging). This is also in contrastto the aggregated data approach of the prior art.

Accordingly, individual winning promotion variables may be isolated andcombined to result in a more effective promotion campaign in one or moreembodiments. Further, the test promotion response data may be analyzedto answer questions related to specific subpopulation attribute(s) orspecific test promotion variable(s). With embodiments of the invention,it is now possible to answer, from the test subpopulation response data,questions such as “How deep of a discount is required to increase by 10%the volume of potato chip purchased by buyers who are 18-25 year-oldmale shopping on a Monday?” or to generate test promotions specificallydesigned to answer such a question. Such data granularity and analysisresult would have been impossible to achieve using the backward-looking,aggregate historical data approach of the prior art.

In one or more embodiments, there is provided a promotional idea modulefor generating ideas for promotional concepts to test. The promotionalidea generation module relies on a series of pre-constructed sentencestructures that outline typical promotional constructs. For example, BuyX, get Y for $Z price would be one sentence structure, whereas Get Y for$Z when you buy X would be a second. It's important to differentiatethat the consumer call to action in those two examples is materiallydifferent, and one cannot assume the promotional response will be thesame when using one sentence structure vs. another. The solution isflexible and dynamic, so once X, Y, and Z are identified, multiple validsentence structures can be tested. Additionally, other variables in thesentence could be changed, such as replacing “buy” with “hurry up andbuy” or “act now” or “rush to your local store to find”. The solutiondelivers a platform where multiple products, offers, and different waysof articulating such offers can be easily generated by a lay user. Theamount of combinations to test can be infinite. Further, the generationmay be automated, saving time and effort in generating promotionalconcepts. In following sections one mechanism, the design matrix, forthe automation of promotional generation will be provided in greaterdetail.

In one or more embodiments, once a set of concepts is developed, thetechnology advantageously a) will constrain offers to only test “viablepromotions”, i.e., those that don't violate local laws, conflict withbranding guidelines, lead to unprofitable concepts that wouldn't bepractically relevant, can be executed on a retailers' system, etc.,and/or b) link to the design of experiments for micro-testing todetermine which combinations of variables to test at any given time.

In one or more embodiments, there is provided an offer selection modulefor enabling a non-technical audience to select viable offers for thepurpose of planning traditional promotions (such as general populationpromotion, for example) outside the test environment. By using filtersand advanced consumer-quality graphics, the offer selection module willbe constrained to only show top performing concepts from the tests, withproduction-ready artwork wherever possible. By doing so, the offerselection module renders irrelevant the traditional, Excel-based orheavily numbers-oriented performance reports from traditional analytictools. The user can have “freedom within a framework” by selecting anyof the pre-scanned promotions for inclusion in an offer to the generalpublic, but value is delivered to the retailer or manufacturer becausethe offers are constrained to only include the best performing concepts.Deviation from the top concepts can be accomplished, but only once thespecific changes are run through the testing process and emerge in theoffer selection windows.

In an embodiment, it is expressly contemplated that the generalpopulation and/or subpopulations may be chosen from social media site(e.g., Facebook™, Twitter™, Google+™ etc.) participants. Social mediaoffers a large population of active participants and often providevarious communication tools (e.g., email, chat, conversation streams,running posts, etc.) which makes it efficient to offer promotions and toreceive responses to the promotions. Various tools and data sourcesexist to uncover characteristics of social media site members, whichcharacteristics (e.g., age, sex, preferences, attitude about aparticular topic, etc.) may be employed as highly granular segmentationcriteria, thereby simplifying segmentation planning.

Although grocery stores and other brick-and-mortar businesses arediscussed in various examples herein, it is expressly contemplated thatembodiments of the invention apply also to online shopping and onlineadvertising/promotion and online members/customers.

These and other features and advantages of embodiments of the inventionmay be better understood with reference to the figures and discussionsthat follow.

FIG. 2A shows, in accordance with an embodiment of the invention, aconceptual drawing of the forward-looking promotion optimization method.As shown in FIG. 2A, a plurality of test promotions 102 a, 102 b, 102 c,102 d, and 102 e are administered to purposefully segmentedsubpopulations 104 a, 104 b, 104 c, 104 d, and 104 e respectively. Asmentioned, each of the test promotions (102 a-102 e) may be designed totest one or more test promotion variables.

In the example of FIG. 2A, test promotions 102 a-102 d are shown testingthree test promotion variables X, Y, and Z, which may represent forexample the size of the packaging (e.g., 12 oz versus 16 oz), the mannerof display (e.g., at the end of the aisle versus on the shelf), and thediscount (e.g., 10% off versus 2-for-1). These promotion variables areof course only illustrative and almost any variable involved inproducing, packaging, displaying, promoting, discounting, etc. of thepackaged product may be deemed a test promotion variable if there is aninterest in determining how the consumer would respond to variations ofone or more of the test promotion variables. Further, although only afew test promotion variables are shown in the example of FIG. 2A, a testpromotion may involve as many or as few of the test promotion variablesas desired. For example, test promotion 102 e is shown testing four testpromotion variables (X, Y, Z, and T).

One or more of the test promotion variables may vary from test promotionto test promotion. In the example of FIG. 2A, test promotion 102 ainvolves test variable X1 (representing a given value or attribute fortest variable X) while test promotion 102 b involves test variable X2(representing a different value or attribute for test variable X). Atest promotion may vary, relative to another test promotion, one testpromotion variable (as can be seen in the comparison between testpromotions 102 a and 102 b) or many of the test promotion variables (ascan be seen in the comparison between test promotions 102 a and 102 d).Also, there are no requirements that all test promotions must have thesame number of test promotion variables (as can be seen in thecomparison between test promotions 102 a and 102 e) although for thepurpose of validating the effect of a single variable, it may be usefulto keep the number and values of other variables (i.e., the controlvariables) relatively constant from test to test (as can be seen in thecomparison between test promotions 102 a and 102 b).

Generally speaking, the test promotions may be generated using automatedtest promotion generation software 110, which varies for example thetest promotion variables and/or the values of the test promotionvariables and/or the number of the test promotion variables to come upwith different test promotions.

In the example of FIG. 2A, purposefully segmented subpopulations 104a-104 d are shown segmented using four segmentation criteria A, B, C, D,which may represent for example the age of the consumer, the householdincome, the zip code, and whether the person is known from pastpurchasing behavior to be a luxury item buyer or a value item buyer.These segmentation criteria are of course only illustrative and almostany demographics, behavioral, attitudinal, whether self-described,objective, interpolated from data sources (including past purchase orcurrent purchase data), etc. may be used as segmentation criteria ifthere is an interest in determining how a particular subpopulation wouldlikely respond to a test promotion. Further, although only a fewsegmentation criteria are shown in connection with subpopulations 104a-104 d in the example of FIG. 2A, segmentation may involve as many oras few of the segmentation criteria as desired. For example,purposefully segmented subpopulation 104 e is shown segmented using fivesegmentation criteria (A, B, C, D, and E).

In the present disclosure, a distinction is made between a purposefullysegmented subpopulation and a randomly segmented subpopulation. Theformer denotes a conscious effort to group individuals based on one ormore segmentation criteria or attributes. The latter denotes a randomgrouping for the purpose of forming a group irrespective of theattributes of the individuals. Randomly segmented subpopulations areuseful in some cases; however they are distinguishable from purposefullysegmented subpopulations when the differences are called out.

One or more of the segmentation criteria may vary from purposefullysegmented subpopulation to purposefully segmented subpopulation. In theexample of FIG. 2A, purposefully segmented subpopulation 104 a involvessegmentation criterion value A1 (representing a given attribute or rangeof attributes for segmentation criterion A) while purposefully segmentedsubpopulation 104 c involves segmentation criterion value A2(representing a different attribute or set of attributes for the samesegmentation criterion A).

As can be seen, different purposefully segmented subpopulation may havedifferent numbers of individuals. As an example, purposefully segmentedsubpopulation 104 a has four individuals (P1-P4) whereas purposefullysegmented subpopulation 104 e has six individuals (P17-P22). Apurposefully segmented subpopulation may differ from anotherpurposefully segmented subpopulation in the value of a singlesegmentation criterion (as can be seen in the comparison betweenpurposefully segmented subpopulation 104 a and purposefully segmentedsubpopulation 104 c wherein the attribute A changes from A1 to A2) or inthe values of many segmentation criteria simultaneously (as can be seenin the comparison between purposefully segmented subpopulation 104 a andpurposefully segmented subpopulation 104 d wherein the values forattributes A, B, C, and D are all different). Two purposefully segmentedsubpopulations may also be segmented identically (i.e., using the samesegmentation criteria and the same values for those criteria) as can beseen in the comparison between purposefully segmented subpopulation 104a and purposefully segmented subpopulation 104 b.

Also, there are no requirements that all purposefully segmentedsubpopulations must be segmented using the same number of segmentationcriteria (as can be seen in the comparison between purposefullysegmented subpopulation 104 a and 104 e wherein purposefully segmentedsubpopulation 104 e is segmented using five criteria and purposefullysegmented subpopulation 104 a is segmented using only four criteria)although for the purpose of validating the effect of a single criterion,it may be useful to keep the number and values of other segmentationcriteria (e.g., the control criteria) relatively constant frompurposefully segmented subpopulation to purposefully segmentedsubpopulation.

Generally speaking, the purposefully segmented subpopulations may begenerated using automated segmentation software 112, which varies forexample the segmentation criteria and/or the values of the segmentationcriteria and/or the number of the segmentation criteria to come up withdifferent purposefully segmented subpopulations.

In one or more embodiments, the test promotions are administered toindividual users in the purposefully segmented subpopulations in such away that the responses of the individual users in that purposefullysegmented subpopulation can be recorded for later analysis. As anexample, an electronic coupon may be presented in an individual user'scomputer-implemented account (e.g., shopping account or loyaltyaccount), or emailed or otherwise transmitted to the smart phone of theindividual. In an example, the user may be provided with an electroniccoupon on his smart phone that is redeemable at the merchant. In FIG.2A, this administering is represented by the lines that extend from testpromotion 102 a to each of individuals P1-P4 in purposefully segmentedsubpopulation 104 a. If the user (such as user P1) makes apromotion-significant response, the response is noted in database 130.

A promotion-significant response is defined as a response that isindicative of some level of interest or disinterest in the goods/servicebeing promoted. In the aforementioned example, if the user P1 redeemsthe electronic coupon at the store, the redemption is stronglyindicative of user P1's interest in the offered goods. However,responses falling short of actual redemption or actual purchase maystill be significant for promotion analysis purposes. For example, ifthe user saves the electronic coupon in his electronic coupon folder onhis smart phone, such action may be deemed to indicate a certain levelof interest in the promoted goods. As another example, if the userforwards the electronic coupon to his friend or to a social networksite, such forwarding may also be deemed to indicate another level ofinterest in the promoted goods. As another example, if the user quicklymoves the coupon to trash, this action may also indicate a level ofstrong disinterest in the promoted goods. In one or more embodiments,weights may be accorded to various user responses to reflect the levelof interest/disinterest associated with the user's responses to a testpromotion. For example, actual redemption may be given a weight of 1,whereas saving to an electronic folder would be given a weight of only0.6 and whereas an immediate deletion of the electronic coupon would begiven a weight of −0.5.

Analysis engine 132 represents a software engine for analyzing theconsumer responses to the test promotions. Response analysis may employany analysis technique (including statistical analysis) that may revealthe type and degree of correlation between test promotion variables,subpopulation attributes, and promotion responses. Analysis engine 132may, for example, ascertain that a certain test promotion variable value(such as 2-for-1 discount) may be more effective than another testpromotion variable (such as 25% off) for 32-oz soft drinks if presentedas an electronic coupon right before Monday Night Football. Suchcorrelation may be employed to formulate a general population promotion(150) by a general promotion generator software (160). As can beappreciated from this discussion sequence, the optimization is aforward-looking optimization in that the results from test promotionsadministered in advance to purposefully segmented subpopulations areemployed to generate a general promotion to be released to the public ata later date.

In one or more embodiments, the correlations ascertained by analysisengine 132 may be employed to generate additional test promotions(arrows 172, 174, and 176) to administer to the same or a different setof purposefully segmented subpopulations. The iterative testing may beemployed to verify the consistency and/or strength of a correlation (byadministering the same test promotion to a different purposefullysegmented subpopulation or by combining the “winning” test promotionvalue with other test promotion variables and administering there-formulated test promotion to the same or a different set ofpurposefully segmented subpopulations).

In one or more embodiments, a “winning” test promotion value (e.g., 20%off listed price) from one test promotion may be combined with another“winning” test promotion value (e.g., packaged in plain brown paperbags) from another test promotion to generate yet another testpromotion. The test promotion that is formed from multiple “winning”test promotion values may be administered to different purposefullysegmented subpopulations to ascertain if such combination would eliciteven more desirable responses from the test subjects.

Since the purposefully segmented subpopulations are small and may besegmented with highly granular segmentation criteria, a large number oftest promotions may be generated (also with highly granular testpromotion variables) and a large number of combinations of testpromotions/purposefully segmented subpopulations can be executed quicklyand at a relatively low cost. The same number of promotions offered asgeneral public promotions would have been prohibitively expensive toimplement, and the large number of failed public promotions would havebeen costly for the manufacturers/retailers. In contrast, if a testpromotion fails, the fact that the test promotion was offered to only asmall number of consumers in one or more segmented subpopulations wouldlimit the cost of failure. Thus, even if a large number of these testpromotions “fail” to elicit the desired responses, the cost ofconducting these small test promotions would still be quite small.

In an embodiment, it is envisioned that dozens, hundreds, or eventhousands of these test promotions may be administered concurrently orstaggered in time to the dozens, hundreds or thousands of segmentedsubpopulations. Further, the large number of test promotions executed(or iteratively executed) improves the statistical validity of thecorrelations ascertained by analysis engine. This is because the numberof variations in test promotion variable values, subpopulationattributes, etc. can be large, thus yielding rich and granulated resultdata. The data-rich results enable the analysis engine to generatehighly granular correlations between test promotion variables,subpopulation attributes, and type/degree of responses, as well as trackchanges over time. In turn, these more accurate/granular correlationshelp improve the probability that a general public promotion createdfrom these correlations would likely elicit the desired response fromthe general public. It would also, over, time, create promotionalprofiles for specific categories, brands, retailers, and individualshoppers where, e.g., shopper 1 prefers contests and shopper 2 prefersinstant financial savings.

FIG. 2B shows, in accordance with an embodiment of the invention, thesteps for generating a general public promotion. In one or moreembodiments, each, some, or all the steps of FIG. 2B may be automatedvia software to automate the forward-looking promotion optimizationprocess. In step 202, the plurality of test promotions are generated.These test promotions have been discussed in connection with testpromotions 102 a-102 e of FIG. 2A and represent the plurality of actualpromotions administered to small purposefully segmented subpopulationsto allow the analysis engine to uncover highly accurate/granularcorrelations between test promotion variables, subpopulation attributes,and type/degree of responses in an embodiment, these test promotions maybe generated using automated test promotion generation software thatvaries one or more of the test promotion variables, either randomly,according to heuristics, and/or responsive to hypotheses regardingcorrelations from analysis engine 132 for example.

In step 204, the segmented subpopulations are generated. In anembodiment, the segmented subpopulations represent randomly segmentedsubpopulations. In another embodiment, the segmented subpopulationsrepresent purposefully segmented subpopulations. In another embodiment,the segmented subpopulations may represent a combination of randomlysegmented subpopulations and purposefully segmented subpopulations. Inan embodiment, these segmented subpopulations may be generated usingautomated subpopulation segmentation software that varies one or more ofthe segmentation criteria, either randomly, according to heuristics,and/or responsive to hypotheses regarding correlations from analysisengine 132, for example.

In step 206, the plurality of test promotions generated in step 202 areadministered to the plurality of segmented subpopulations generated instep 204. In an embodiment, the test promotions are administered toindividuals within the segmented subpopulation and the individualresponses are obtained and recorded in a database (step 208).

In an embodiment, automated test promotion software automaticallyadministers the test promotions to the segmented subpopulations usingelectronic contact data that may be obtained in advance from, forexample, social media sites, a loyalty card program, previous contactwith individual consumers, or potential consumer data purchased from athird party, etc. The responses may be obtained at the point of saleterminal, or via a website or program, via social media, or via an appimplemented on smart phones used by the individuals, for example.

In step 210, the responses are analyzed to uncover correlations betweentest promotion variables, subpopulation attributes, and type/degree ofresponses.

In step 212, the general public promotion is formulated from thecorrelation data, which is uncovered by the analysis engine from dataobtained via subpopulation test promotions. In an embodiment, thegeneral public promotion may be generated automatically using publicpromotion generation software which utilizes at least the test promotionvariables and/or subpopulation segmentation criteria and/or test subjectresponses and/or the analysis provided by analysis engine 132.

In step 214, the general public promotion is released to the generalpublic to promote the goods/services.

In one or more embodiments, promotion testing using the test promotionson the segmented subpopulations occurs in parallel to the release of ageneral public promotion and may continue in a continual fashion tovalidate correlation hypotheses and/or to derive new general publicpromotions based on the same or different analysis results. If iterativepromotion testing involving correlation hypotheses uncovered by analysisengine 132 is desired, the same test promotions or new test promotionsmay be generated and executed against the same segmented subpopulationsor different segmented subpopulations as needed (paths 216/222/226 or216/224/226 or 216/222/224/226). As mentioned, iterative promotiontesting may validate the correlation hypotheses, serve to eliminate“false positives” and/or uncover combinations of test promotionvariables that may elicit even more favorable or different responsesfrom the test subjects.

Promotion testing may be performed on an on-going basis using the sameor different sets of test promotions on the same or different sets ofsegmented subpopulations as mentioned (paths 218/222/226 or 218/224/226or 218/222/224/226 or 220/222/226 or 220/224/226 or 220/222/224/226).

FIG. 3A shows in greater detail, in accordance with an embodiment of theinvention, the administering step 206 of FIG. 2 from the user'sperspective. In step 302, the test promotion is received from the testpromotion generation server (which executes the software employed togenerate the test promotion). As examples, the test promotion may bereceived at a user's smart phone or tablet (such as in the case of anelectronic coupon or a discount code, along with the associatedpromotional information pertaining to the product, place of sale, timeof sale, etc.) or in a computer-implemented account (such as a loyaltyprogram account) associated with the user that is a member of thesegmented subpopulation to be tested or via one or more social mediasites. In step 304, the test promotion is presented to the user. In step306, the user's response to the test promotion is obtained andtransmitted to a database for analysis.

FIG. 3B shows in greater detail, in accordance with an embodiment of theinvention, the administering step 206 of FIG. 2 from the forward-lookingpromotion optimization system perspective. In step 312, the testpromotions are generated using the test promotion generation server(which executes the software employed to generate the test promotion).In step 314, the test promotions are provided to the users (e.g.,transmitted or emailed to the user's smart phone or tablet or computeror shared with the user using the user's loyalty account). In step 316,the system receives the user's responses and stores the user's responsesin the database for later analysis.

FIG. 4 shows various example segmentation criteria that may be employedto generate the purposefully segmented subpopulations. As show in FIG.4, demographics criteria (e.g., sex, location, household size, householdincome, etc.), buying behavior (category purchase index, most frequentshopping hours, value versus premium shopper, etc.), past/currentpurchase history, channel (e.g., stores frequently shopped at,competitive catchment of stores within driving distance), behavioraleconomics factors, etc. can all be used to generate with a high degreeof granularity the segmented subpopulations. The examples of FIG. 4 aremeant to be illustrative and not meant to be exhaustive or limiting. Asmentioned, one or more embodiments of the invention generate thesegmented subpopulations automatically using automated populationsegmentation software that generates the segmented subpopulations basedon values of segmentation criteria.

FIG. 5 shows various example methods for communicating the testpromotions to individuals of the segmented subpopulations being tested.As shown in FIG. 5, the test promotions may be mailed to theindividuals, emailed in the form of text or electronic flyer or couponor discount code, displayed on a webpage when the individual accesseshis shopping or loyalty account via a computer or smart phone or tablet.Redemption may take place using, for example, a printed coupon (whichmay be mailed or may be printed from an electronic version of thecoupon) at the point of sale terminal, an electronic version of thecoupon (e.g., a screen image or QR code), the verbal providing or manualentry of a discount code into a terminal at the store or at the point ofsale. The examples of FIG. 5 are meant to be illustrative and not meantto be exhaustive or limiting. One or more embodiments of the inventionautomatically communicate the test promotions to individuals in thesegmented subpopulations using software thatcommunicates/email/mail/administer the test promotions automatically. Inthis manner, subpopulation test promotions may be administeredautomatically, which gives manufacturers and retailers the ability togenerate and administer a large number of test promotions with lowcost/delay.

FIG. 6 shows, in accordance with an embodiment, various examplepromotion-significant responses. As mentioned, redemption of the testoffer is one strong indication of interest in the promotion. However,other consumer actions responsive to the receipt of a promotion may alsoreveal the level of interest/disinterest and may be employed by theanalysis engine to ascertain which test promotion variable is likely orunlikely to elicit the desired response. Examples shown in FIG. 6include redemption (strong interest), deletion of the promotion offer(low interest), save to electronic coupon folder (mild to stronginterest), clicked to read further (mild interest), forwarding to selfor others or social media sites (mild to strong interest). As mentioned,weights may be accorded to various consumer responses to allow theanalysis engine to assign scores and provide user-interest data for usein formulating follow-up test promotions and/or in formulating thegeneral public promotion. The examples of FIG. 6 are meant to beillustrative and not meant to be exhaustive or limiting.

FIG. 7 shows, in accordance with an embodiment of the invention, variousexample test promotion variables affecting various aspects of a typicaltest promotion. As shown in FIG. 7, example test promotion variablesinclude price, discount action (e.g., save 10%, save $1, 2-for-1 offer,etc.), artwork (e.g., the images used in the test promotion to drawinterest), brand (e.g., brand X potato chips versus brand Y potatochips), pricing tier (e.g., premium, value, economy), size (e.g., 32 oz,16 oz, 8 oz), packaging (e.g., single, 6-pack, 12-pack, paper, can,etc.), channel (e.g., email versus paper coupon versus notification inloyalty account). The examples of FIG. 7 are meant to be illustrativeand not meant to be exhaustive or limiting. As mentioned, one or moreembodiments of the invention involve generating the test promotionsautomatically using automated test promotion generation software byvarying one or more of the test promotion variables, either randomly orbased on feedback from the analysis of other test promotions or from theanalysis of the general public promotion.

FIG. 8 shows, in accordance with an embodiment of the invention, ageneral hardware/network view of the forward-looking promotionoptimization system 800. In general, the various functions discussed maybe implemented as software modules, which may be implemented in one ormore servers (including actual and/or virtual servers). In FIG. 8, thereis shown a test promotion generation module 802 for generating the testpromotions in accordance with test promotion variables. There is alsoshown a population segmentation module 804 for generating the segmentedsubpopulations in accordance with segmentation criteria. There is alsoshown a test promotion administration module 806 for administering theplurality of test promotions to the plurality of segmentedsubpopulations. There is also shown an analysis module 808 for analyzingthe responses to the test promotions as discussed earlier. There is alsoshown a general population promotion generation module 810 forgenerating the general population promotion using the analysis result ofthe data from the test promotions. There is also shown a module 812,representing the software/hardware module for receiving the responses.Module 812 may represent, for example, the point of sale terminal in astore, a shopping basket on an online shopping website, an app on asmart phone, a webpage displayed on a computer, a social media newsfeed, etc. where user responses can be received.

One or more of modules 802-812 may be implemented on one or moreservers, as mentioned. A database 814 is shown, representing the datastore for user data and/or test promotion and/or general publicpromotion data and/or response data. Database 814 may be implemented bya single database or by multiple databases. The servers and database(s)may be coupled together using a local area network, an intranet, theinternet, or any combination thereof (shown by reference number 830).

User interaction for test promotion administration and/or acquiring userresponses may take place via one or more of user interaction devices.Examples of such user interaction devices are wired laptop 840, wiredcomputer 844, wireless laptop 846, wireless smart phone or tablet 848.Test promotions may also be administered via printing/mailing module850, which communicates the test promotions to the users via mailings852 or printed circular 854. The example components of FIG. 8 are onlyillustrative and are not meant to be limiting of the scope of theinvention. The general public promotion, once generated, may also becommunicated to the public using some or all of the user interactiondevices/methods discussed herein.

As can be appreciated by those skilled in the art, providing aresult-effective set of recommendations for a generalized publicpromotion is one of the more important tasks in test promotionoptimization.

In one or more embodiments, there are provided adaptive experimentationand optimization processes for automated promotion testing. Testing issaid to be automated when the test promotions are generated in themanner that is likely produce the desired response consistent with thegoal of the generalized public promotion.

For example, if the goal is to maximize profit for the sale of a certainnewly created brand of potato chips, embodiments of the inventionoptimally and adaptively, without using required human intervention,plan the test promotions, iterate through the test promotions to testthe test promotion variables in the most optimal way, learn and validatesuch that the most result-effective set of test promotions can bederived, and provide such result-effective set of test promotions asrecommendations for generalized public promotion to achieve the goal ofmaximizing profit for the sale of the newly created brand of potatochips.

The term “without required human intervention” does not denote zerohuman intervention. The term however denotes that the adaptiveexperimentation and optimization processes for automated promotiontesting can be executed without human intervention if desired. However,embodiments of the invention do not exclude the optional participationof humans, especially experts, in various phases of the adaptiveexperimentation and optimization processes for automated promotiontesting if such participation is desired at various points to injecthuman intelligence or experience or timing or judgment in the adaptiveexperimentation and optimization processes for automated promotiontesting process. Further, the term does not exclude the optionalnonessential ancillary human activities that can otherwise also beautomated (such as issuing the “run” command to begin generating testpromotions or issuing the “send” command to send recommendationsobtained).

II. Intelligent Promotion Design

Now that the broad concept of forward looking promotion optimization hasbeen discussed in considerable detail, attention shall now be focusedupon the ability to assist users, with the selection of promotions foran intelligent promotion experimental design. By designing promotionaltesting to be more efficient, lower testing costs are incurred, and themost effective promotions may be quickly zeroed in upon.

In FIG. 9, example framework for the intelligent promotion designgeneration and administrations system 900 is provided. Of note, in thissystem the intelligent offer design system 910 interfaces with a server920 that aggregates data collected from retailers 930 a-z as well asother third party data sources 940. These third party data sources 940may include web based platforms such as Facebook or other social mediasites, email applications, or applications operating on mobile devices.Between the retailer data sources and the third party data sources,information regarding a user's redemption, opening, sharing or otheractivity related to a promotion may all be captured for offer efficacyanalysis. This enables the intelligent offer design system 910, whichcouples to the data aggregation server 920, to leverage data fromdifferent data sources in order to effectively design and administerpromotional offers. This results in a highly scalable, and effectivepromotion experimentation framework that enables concurrent experimentsof multiple offers across a wide number of consumer segments, showngenerally as consumers A-N at 905 a-n respectively.

The intelligent offer design system 910 includes much of the samefunctional components as identified in FIG. 8. However, rather thanprinting and mailing the promotions, the present system includes APIsthat allow the transmission and display of the offers to a wide contextof consumers 905 a-n. For example the promotions may consist of email,text messages, Facebook feeds (or other website based feeds), or withinspecialized applications the consumers 905 a-n may leverage. Forexample, many consumers have applications related to a specific retaileror brand on their mobile devices. The intelligent offer design system910 may have appropriate API that allow the offers to be populatedwithin relevant applications in the mobile devices of the consumers.

The intelligent offer design system 910 relies upon retailer collecteddata 930 a-b, as well as third party data 940 as noted above for theproper analysis of offer efficacy. As noted previously, third party data940 may include data collected from other promotional platforms andoffer streams. The retailers data 930 a-b typically includes informationrelating to the stores 932 that belong to the retailer. This may includestore inventories, location, operating hours, and other pertinentinformation. Likewise, user information 934 is included in the retailerdata. User information typically includes user account information, andmay also include data collected regarding the user's purchase history,location/date information, and any other profile data that has beenoffered or collected regarding the user (e.g., age, sex, income level,ethnicity, familial status, etc.). Lastly, the retailer data may includeoffer information 936 that has been previously tested and the results ofthe offers. This information is critical to the optimization of offersin order to hone in on the most effective promotions.

The intelligent offer design system 910, by its very nature, compilesvast amounts of information and therefore may include significantprocessing power, consisting of arrays of computer processors performingparallel computations. Likewise, given the vast data collected regardinghistorical offers, and possible new offer types, the intelligent offerdesign system 910 includes significant data storage. This may includemultiple storage devices including hard drives and/or solid statedrives. In some embodiments, tiers of storage are required to meet thestorage requirements of the intelligent offer design system 910.

In some embodiments, the entire intelligent offer design system 910 maybe centralized within one server device. However, given the computingdemands involved in these methods, often a distributed computingenvironment is required to complete the desired offer design andadministration. Particularly, often the activities of intelligentpromotion design is performed in one computing device (or cluster ofdevices) while the administration of promotions is relegated to aseparate computing system. In yet other embodiments, a third computingsystem, in communication with the other systems, performs the analysisand modeling of promotional efficacy upon receipt of information fromthe retailers and other third part data sources.

FIG. 10 shows, in accordance with some embodiments, an example blockdiagram of the intelligent offer design system 910 in terms of itslogical processes. As noted above, even within this process of designingpromotions for effective experimentation, the separate stages may becompleted in a central computing system, or may be handles by morespecialized and discrete computing systems. For example, the pricingheuristic system 1010 may be less computationally extensive than theranking system, in some embodiments, but may require vast storagecapacity based upon the very large numbers of variable valuesconsidered. The ranking system 1020, in contrast, may be required toundergo complex and lengthy computations on a reduced dataset providedfrom the heuristic system 1010, and thus require more available randomaccess memory and processing power, yet lower overall storage capacity.

The pricing heuristics system 1010 generates a listing of all availablevariables and the possible values, within user defined limits. Thislisting of variable values is then subjected to a pruning processwhereby user inputted constraints, and/or constraints derived from aretailer's business goals, brand requirements, rounding rules, or thelike, are applied. This results in a paired down set of variable valuescombinations that explores the entire design space of possible offersthat do not run afoul of a rule constraint.

This listing of all possible variable values combinations forming offersthat do not infringe upon the applied rules are then provided to theranking system 1020 that applies trained models on to rank the top Xpercentage of the offer combinations. Typically the top 10-20% of offersis selected. In alternate embodiments the system is configured to selectout the top 4 or 5 offers from the available combinations.

The models employed utilize machine learning to select the top offers.Historical data from retailers and third party data is utilized to trainthe models. These models are continually updated as additional data iscollected. As noted, different activity by a user may be weighteddifferently in the determination of which promotions are most effective.This includes weighting redemption rates, shares, downloads, and viewsof a given offer differently. It should be understood that any suitablemodel for ranking the offers could be employed; however, one example ofa means for scoring the offers will be discussed in detail furtherbelow.

All offer combinations that are not selected as the top offers remaineligible for inclusion in the promotional experiment design, and anadditional subset of offers are selected by the optimal experimentalselection algorithms 1030 to maximize particular criteria, as will bediscussed in further detail below. The top offers, and the selectedsubset of offers are then aggregated as a set of offers 1040 forpresentation to the consumer for forward looking experimentation asalready covered in detail above. This promotion experimentation utilizesconcurrent and staggered small tests of particular offers againstvarious consumer subsets in order to explore the incremental impact eachvariable value combination, and sets of variable values, have on theoverall performance of the offer. In this manner, the most effectivecombination of variable values may be identified, for a given consumersubpopulation, and the general promotion may be administered to a widerdistribution than any of the test promotions.

The process of generating an intelligent promotional design, asdescribed in broad terms above, shall now be explored in greater detail.As noted, a given promotion may include a given set of variables, eachof which may include many possible values (referred to as a variablevalue pair). A set of variable value pairs together defines a givenpromotion. The variables may be user defined, or may be intrinsic to agiven promotion template and/or brand of product. For example, an emailpromotion template for a brand of potato chips may include the followingvariables: package size, image, offer type, percentage discount,background color, lettering font, lettering size and quantity, forexample. FIG. 11A illustrates how a table of each variable valuecombination may be compiled to provide the entire design space for agiven promotion, shown generally at 1100A. This listing of all possiblecombinations may be infinite if not initially limited in some basicways. For example the system may enforce that the discounts have aminimal value of 0% and only be incremented at a 10% interval. Further,the brand may include some initial constraints, such as the maximumdiscount is 40%. Thus, for this variable “discount” the total values are10%, 20%, 30% and 40%. Likewise, it is possible that only certain fonts,font sizes, colors and images are available.

However, even at only 3 or 4 values possible for a given variable, in anoffer that contemplates 6 different variables it is possible to have atotal of 729-4096 combinations of possible offers. Having more variablesor values rapidly increases the possible numbers of possible offers. Forthis reason, for the sake of clarity the following examples will belimited to only three variables with two, four and three possiblevalues, respectively. A variable value tables showing these possiblecombinations of offers is provided at 1100B of FIG. 11B. Given thisrelatively limited variable value set, a manageable 24 possiblecombinations are possible.

Next additional user constraints and rounding rules are applied to thevariable value combinations in order to limit the number of possibleoffers further. These constraints may be user defined, such as “do notcombine a dollar off type promotion with a 40% off discount depth.”Additionally the rounding rules ensure the discount type and depthresults in acceptable values. For example, if the percentage off anddiscount type results in a price that is not within a given percentageof an approved price format, then the combination of variable values maybe removed from the listing of viable combinations. For example, therules may state that any discount must be within three cents of a halfdollar denomination, and when applied, the price listed in the promotionis rounded to that half dollar amount. In such an example, a widgetcosting $5.75 normally would be $4.60 at a 20% discount, and $4.03 at a30% discount. The rounding rules may disqualify the 20% discount sincethe promotional price does not fit into an acceptable dollar amount,whereas the 30% off offer may be kept, and rounded to a price of $4 perwidget, in this limited example. Other examples of heuristic rules mayprevent high discounts at high buy quantities, or similar rules.Overall, the rounding rules applied may be directed toward the goals ofincreasing total sales, household penetration, increased sales at thesame price point, or increased margin.

If increase in total sales is the selected goal, the heuristic ruleapplied may include incremental deep discounts above a control level. Ifthe selected goal is household penetration, then the rules applied mayoverweight low quantity offers but not high quantity offers. Increasedsales at a fixed price point may impose rules which limit the discountand only vary other variables such as quantity, and offer structure. Anincreased margin goal may be accompanied by heuristic rules that haveincremental price increases based upon upper and lower bounds.

In some embodiments, the above heuristic rules may be predefined withinthe system, and the user merely is required to input their desired goalfrom a drop down menu, radial button selection or the like. Upon goalselection, the system may automatically apply the stored rules to thevariable value combinations in order to eliminate particular offers. Inalternate embodiments, the consumer space involved may be utilized todefine the goals. For example if the product selected is a householdconsumable good, the system may default to a heuristic rule thatoverweight low quantity offers but not high quantity offers without anyadditional user intervention. In contrast, newly introduced products maybe subject to incremental deep discounts above a control level, againwithout any user input.

The final results of the application of rounding rules and heuristicrules is the elimination of some subset of the offers, as may be seen at1100C of FIG. 11C. In this example, the rule applied does not allow forvariable C having its second value. These eliminated offers are shadedin this example illustration. All remaining sixteen viable offercombinations are then extracted an numbered accordingly, as may be seenat 1200A of FIG. 12A. This offer listing is then subjected to scoringand ranking. The tope percentage or number of the offers based upon thisscoring are then identified, as seen in 1200B of FIG. 12B. The number orpercentage of offers selected as “the top offers” may be userconfigured, or may be predefined. In some systems the top 20% of offersor the top 4 or 5 offers are selected. In this particular example, fouroffers are determined to be the top offers: offer 3, 5, 12 and 13.

The scoring of offers may be computationally intensive, in someembodiments. The scoring of the offers utilizes machine learning, asdiscussed above. It starts with a training set of offers for whichmeasured efficacy rates have been collected. FIG. 13 provides an exampletable of four such training set of offers for illustrative purposes,shown generally at 1300.

In this scoring system the training offers are identified in the firstcolumn by a designator. The next three columns show the relevant valuesfor each variable used in the training set. Again, note that theseexamples training set offers only have three variables for the sake ofsimplicity and understanding. In a real-world computation many morevariables with many values could be considered. The results of thevariable values are then coded into a matrix, as shown in the subsequentthree columns. In this abstraction, variable A, B and C's first value isgiven a designation of 0, and the second value is a 1. This defines avector for the variables of the given test offer (e.g., test offer 1 hasa vector of [0,0,1], whereas offer 2 has a vector of [0,1,0]). Ingeneral, categorical variables are transformed into k_(i) binary vectorof columns for every i through N number of variables. For example,assume that in FIG. 13 there also was a variable D which has four totalvalues representing four different offer types, (e.g., {Percent Off,DollarOff, BOGO, Total Price}) indicating the offer type of each offer 1thru 4 respectively. The binary representation would break out thissingle 4-valued column into 4 columns represented by the following:D-PercentOff={1,0,0,0}, D-DollarOff={0,1,0,0}, D-BOGO={0,0,1,0} andD-TotalPrice={0,0,0,1}.

The following column is a measured metric of offer success. In thisexample this is the klip rate. Klip rate is the rate of clipping of theoffer on the website ‘Klip′em’, but may be abstracted to be the rate anoffer is selected/downloaded or saved on any promotional platform. Thisclip rate is the number of ‘clips’ divided by the number of impressionsof the offer. Clips may be the number of ‘clicks’ on the digital offerand impressions are the number of views consumers made of the offer. Inalternate embodiments, the metric used to measure offer success may beredemption rate, shares, saves, views, or an amalgamation of any ofthese metrics. For example, in some embodiments, and of these activitiesmay be measured and a weighted average taken in order to determine afinal efficacy score. For example a redemption may be weighted heavily,while a saving or sharing given significant weight as well (possiblyhalf the weight of a redemption), whereas simply viewing the promotionwould only contribute slightly to the metric of offer success. Inaddition to the klip rate measured in this example an impression scoreis likewise compiled. Impressions simply normalizes the klip signalmeasurement into klip rate (or clip rate)−a value ranging from 0 to 1.Alternatively a composite score may be generated which includes thenumber of ‘likes’, posts, shares and klips which would individually benormalized by impressions and then tuned by individual weightingconstants.

From the table compiled in FIG. 13 for the training offers, a derivativetable of pair-wise offer comparisons is made, as seen at 1400 of FIG.14. The pairs are given a designation (pair ID) in the first column. Allof the training offers are paired in the manner shown in the secondcolumn. The variable vectors of the pairs are then subtracted from oneanother to provide differential values, as seen in the following threecolumns. For example, for the pair ID 1, the pair is test offer 2 beingsubtracted from test offer 1 (1-2). As noted above the vector for testoffer 1 is [0,0,1]. The vector for test offer 2 is [0,1,0] Thus, pair1-2 is [0,0,1]−[0,1,0]=[0,−1,1]. This is performed for every offer paircombination. The next four columns in this derived table indicate whichoffer is being added or subtracted in the pair for reference purposes.Again, for pair ID 1, offer 1 is being included (therefore is given avalue of 1) and offer 2 is being subtracted from offer 1 (therefore hasa value of −1). Offer 3 and 4 are not in this pair (therefore theirvalues are 0).

The delta and normalized score are the final two columns of thisderivative table shown in FIG. 14. The delta is merely a subtraction ofthe klip rates between the pair combination. The normalized score is ascaling of this delta based on a normalized teststatistic=delta/SE_(data), whereSE_(data)=((kliprate₂*(1−kliprate₂)/impressions₂)+(kliprate₁*(1−kliprate₁)/impressions₁))^(0.5),where delta=kliprate₂−kliprate₁.

Using the normalized score, a model may be generated via machinelearning. This model may either be as a decision tree or a perceptronneural network model based upon the scale of the data involved. Decisiontree and neural network algorithm are known, and may be employed in thegeneration of the model. Generally, for the data originating from asingle client (brand or retailer) the relatively small scale of data isconducive to utilization of a decision tree model, whereas when theinformation from multiple clients are being aggregated a neural networkmodel is more effective. FIG. 15 provides an example of a where the testoffers are transformed back into the original set using the generatedmodel via an ordinary least squares method or other linear regressiontechnique, as seen at 1500. In this table the coefficients are relativeto the test offer 4. The estimate values and/or t-value may be utilizedas the score for the offer. Likewise, all offers identified in the tableof FIG. 12A may each be scored according to the variable values of theoffer in light of the model, and ranked relative to their resultingscore. In this manner the top offers may be identified for usage in thepromotional experiments.

Subsequently, the offers that were not designated as the “top” offersmay be further analyzed in order to select an additional set of offersto “flesh out” the offer set for promotional experimentation. FIG. 16provides the example list of offers with the ‘top’ offers shaded (asthey are already part of the promotional experimental design, and anadditional four offers selected to round out the promotional design, asseen at 1600. In this example, all offers are considered as candidatesfor selection for addition to the promotions design; however insituations where there are many offers available (due to more variables,more values for the variables, or less pruning during the heuristicstep), potentially only a subset of the total offers may be availablefor consideration to be selected to the promotion experiment design.

For example, in some embodiments only the top 20 or 30 offers, asdetermined during the ranking step, may be considered for selection. Inthese examples, the top 4 (or 20%) of offers are added to the promotiondesign set, and the offers ranks 5-25 may be available for selecting theother 4 promotions to add to the promotion experimental design. Ofcourse the above numbers are purely illustrative for this example, it isentirely possible that 10, 20 or more offers may be desired to beincluded in the promotion design. In such cases it is entirely possibleto select 4-15 ‘top’ promotions and an additional 4-20 other promotionsfrom the remaining list of available promotions.

While it is entirely possible that the remaining offers that areselected after ranking the promotions is performed in a randomizedmanner, in some embodiments the remaining promotions are selected verypurposefully in order to maximize particular criteria. These criteriaenables the most effective experimental design during the test/discoveryphase of the promotion rollout. Looking again at FIG. 16, the fourselected promotions are chosen to optimize the following criteria: 1)equal number of each value is selected, and 2) maximization oforthogonality between the selected variable value combinations of theselected offers.

In FIG. 16, the four offers selected have the following variable valuevectors: [A1, B1, C1], [A1, B3, C3], [A2, B2, C1], and [A2, B4, C3]. Inthis example, A1 is present in the same frequency as A2. Likewise B1,B2, B3 and B4 are each present once; and C1 and C3 are equally present.Thus, number of instance of each variable value combination is equal.Likewise, there is perfect orthogonality in this present example; novariable value combination is repeated between the various selectedoffers.

FIGS. 17A and 17B provide examples of how to calculate thisorthogonality in order to illustrate a “good” selection of offers(maximized orthogonality) versus a less-optimal offer selection withlower orthogonality, respectively. In FIG. 17A an example of four offerswith only two variables and two values per variable is provided, seengenerally at 1700A. These offers are abstracted into vectors, aspreviously discussed, and the set of offers are combined to form amatrix of values given by:

$X = \begin{bmatrix}0 & 0 \\1 & 0 \\0 & 1 \\1 & 1\end{bmatrix}$

The optimization seeks to minimize (X^(T)X)⁻¹ or equivalently maximizedeterminant of X^(T)X. Thus we find:

${X^{T}X} = {{\begin{bmatrix}0 & 1 & 0 & 1 \\0 & 0 & 1 & 1\end{bmatrix}\begin{bmatrix}0 & 0 \\1 & 0 \\0 & 1 \\1 & 1\end{bmatrix}} = \begin{bmatrix}2 & 1 \\1 & 2\end{bmatrix}}$ $\begin{matrix}{\left( {X^{T}X} \right)^{- 1} = {\frac{1}{\det\left( {X^{T}X} \right)}\begin{bmatrix}2 & {- 1} \\{- 1} & 2\end{bmatrix}}} \\{= {\frac{1}{3}\begin{bmatrix}2 & {- 1} \\{- 1} & 2\end{bmatrix}}}\end{matrix}$

Therefore we have a determinant of 3. Thus the covariance is found by:

${{cov}(b)} = {{s^{2}\left( {X^{T}X} \right)}^{- 1} = {s^{2}{\frac{1}{3}\begin{bmatrix}2 & {- 1} \\{- 1} & 2\end{bmatrix}}}}$

In contrast for FIG. 17B, the following matrix is found:

$X = \begin{bmatrix}0 & 0 \\1 & 1 \\0 & 1 \\1 & 1\end{bmatrix}$

Therefore:

${X^{T}X} = {{\begin{bmatrix}0 & 1 & 0 & 1 \\0 & 1 & 1 & 1\end{bmatrix}\begin{bmatrix}0 & 0 \\1 & 1 \\0 & 1 \\1 & 1\end{bmatrix}} = \begin{bmatrix}2 & 2 \\2 & 3\end{bmatrix}}$ $\begin{matrix}{\left( {X^{T}X} \right)^{- 1} = {\frac{1}{\det\left( {X^{T}X} \right)}\begin{bmatrix}3 & {- 2} \\{- 2} & 2\end{bmatrix}}} \\{= {\frac{1}{\left( {3 \times 2} \right) - \left( {2 \times 2} \right)}\begin{bmatrix}3 & {- 2} \\{- 2} & 2\end{bmatrix}}} \\{= {\frac{1}{2}\begin{bmatrix}3 & {- 2} \\{- 2} & 2\end{bmatrix}}}\end{matrix}$

This results in a determinant of 2, and the covariance is found by:

$\begin{matrix}{{{cov}(b)} = {s^{2}\left( {X^{T}X} \right)}^{- 1}} \\{= {s^{2}{\frac{1}{2}\begin{bmatrix}3 & {- 2} \\{- 2} & 2\end{bmatrix}}}}\end{matrix}$

Thus it can be seen that by maximizing the determinant of a given set ofoffers, the covariance of the betas are minimized. This results in areduction of variance. Smaller covariances yield the ability to isolatethe statistical significance of the betas of the factors, which for thisexample is offer structure and discount. In this manner the selection ofoffers may be optimized in a manner that allows for the bestexperimental design.

Lastly, FIG. 18 shows, in accordance with some embodiments, a flowchart1800 of an example method for the generation and application ofintelligent promotional designs. As discussed, initially the variablesfor the promotional campaign are selected, at 1810. This selectionprocess may be facilitated through a promotion design wizard or otheruser friendly application. In some embodiments templates for promotionsmay be selected by the user and the variables associated with thetemplate may be utilized.

The values for each of the variables are next defined, at 1820. Again,the system may include preset rules relating to upper and lower bounds,increment levels, or other selections. Other value constraints may beincorporated based upon the brands being promoted, channel of promotionor template choices. The user may likewise define basic constraints tolimit the overall number of possible combinations.

Next a matrix or table of all variable value combinations for all theoffers is generated, at 1830. This table may resemble that as seen inFIG. 11B. Heuristics may then be applied to the offer matrix toeliminate some of the offer combinations, at 1840. These heuristics mayinclude user generated rules, or may include preset rules that comportto a given business goal selected by the user. In yet other embodiments,the heuristics applied may be dependent upon the product being promoted,the consumer base being targeted, lifecycle of the product, externalbusiness climate/economic factors, time of the year (e.g., pre-BlackFriday promotions), or similar factors. Additionally rounding rules maybe employed to avoid promotions that contain strange pricing structuresor values. This results in a further reduced set of possible promotionscombinations (although in many situations this still may includemultiple dozens or even hundreds of possible offer combinations).

This reduced set of possible offers is then subjected to scoring usingmachine learned models, at 1850, and are then subsequently rankedaccording to their scores. This scoring and ranking process is describedin greater detail in relation to the process illustrated in FIG. 19. Thescoring process starts with the feedback received from a set of testoffers (also referred to as training offers). The variable values foreach of these training offers are converted into a set of vectors, at1910. As noted, the success of these training offers is also measured,at 1920, using klip rates, redemption rates, views, sharing, saving ofthe offers, impressions, or any weighted composite of these measures.

Next the training offers are paired in all possible combinations, at1930, and the vector values for the two offers in the pair aresubtracted from one another, at 1940, to generate differential vectorsfor each pair. A normalized score is then generated based upon thesedifferential vectors, at 1950. The model is then generated utilizingmachine learning, at 1960. For smaller scale datasets, the model mayconsist of a decision tree. For larger scale data sets, such as frommultiple clients, a neural network model may be more appropriate.

After the model has been generated, linear regression may be utilizedand applied to the offer set resulting from the application ofheuristics, at 1970. This modeling results in an estimate and t-value,either of which may be employed to score the offer. Offers may then beranked based upon their respective scores.

Returning to FIG. 18, after ranking the offers a set of the highestranked offers are selected for inclusion in the promotional campaign, at1860. The number of ‘top offers’ selected may be a predefined number,such as 4 or 5 offers, or may be a percentage of the total offercombinations (e.g., top 20% for example). The remaining offers (or somesubset of the best ranking offers not selected as a ‘top offer’) maythen be analyzed for inclusion in the promotional campaign, at 1870. Asdiscussed above, orthogonality, as determined by minimizing thecovariance/maximizing the determinant for a matrix of the variablevalues of the offers. Likewise, equal numbers of values for eachvariable is desired.

These top offers and selected remaining offers are then administered, at1880, as a set of test promotions to sets of segmented consumers, asdiscussed extensively above. Feedback from these test offers iscollected thereby allowing for further model refinement, at 1890, andultimately for the generation of a general promotion for widerdistribution once the modeling has been sufficiently validated.

III. System Embodiments

Now that the systems and methods for the generation of a intelligentpromotional design systems and methods have been described, attentionshall now be focused upon apparatuses capable of executing the abovefunctions. To facilitate this discussion, FIGS. 20A and 20B illustrate aComputer System 2000, which is suitable for implementing embodiments ofthe present invention. FIG. 20A shows one possible physical form of theComputer System 2000. Of course, the Computer System 2000 may have manyphysical forms ranging from a printed circuit board, an integratedcircuit, and a small handheld device up to a huge super computer.Computer system 2000 may include a Monitor 2002, a Display 2004, aHousing 2006, a Disk Drive 2008, a Keyboard 2010, and a Mouse 2012. Disk2014 is a computer-readable medium used to transfer data to and fromComputer System 2000.

FIG. 20B is an example of a block diagram for Computer System 2000.Attached to System Bus 2020 are a wide variety of subsystems.Processor(s) 2022 (also referred to as central processing units, orCPUs) are coupled to storage devices, including Memory 2024. Memory 2024includes random access memory (RAM) and read-only memory (ROM). As iswell known in the art, ROM acts to transfer data and instructionsuni-directionally to the CPU and RAM is used typically to transfer dataand instructions in a bi-directional manner. Both of these types ofmemories may include any suitable of the computer-readable mediadescribed below. A Fixed Disk 2026 may also be coupled bi-directionallyto the Processor 2022; it provides additional data storage capacity andmay also include any of the computer-readable media described below.Fixed Disk 2026 may be used to store programs, data, and the like and istypically a secondary storage medium (such as a hard disk) that isslower than primary storage. It will be appreciated that the informationretained within Fixed Disk 2026 may, in appropriate cases, beincorporated in standard fashion as virtual memory in Memory 2024.Removable Disk 2014 may take the form of any of the computer-readablemedia described below.

Processor 2022 is also coupled to a variety of input/output devices,such as Display 2004, Keyboard 2010, Mouse 2012 and Speakers 2030. Ingeneral, an input/output device may be any of: video displays, trackballs, mice, keyboards, microphones, touch-sensitive displays,transducer card readers, magnetic or paper tape readers, tablets,styluses, voice or handwriting recognizers, biometrics readers, motionsensors, brain wave readers, or other computers. Processor 2022optionally may be coupled to another computer or telecommunicationsnetwork using Network Interface 2040. With such a Network Interface2040, it is contemplated that the Processor 2022 might receiveinformation from the network, or might output information to the networkin the course of performing the above-described intelligent promotionaldesign generation and administration. Furthermore, method embodiments ofthe present invention may execute solely upon Processor 2022 or mayexecute over a network such as the Internet in conjunction with a remoteCPU that shares a portion of the processing.

Software is typically stored in the non-volatile memory and/or the driveunit. Indeed, for large programs, it may not even be possible to storethe entire program in the memory. Nevertheless, it should be understoodthat for software to run, if necessary, it is moved to a computerreadable location appropriate for processing, and for illustrativepurposes, that location is referred to as the memory in this disclosure.Even when software is moved to the memory for execution, the processorwill typically make use of hardware registers to store values associatedwith the software, and local cache that, ideally, serves to speed upexecution. As used herein, a software program is assumed to be stored atany known or convenient location (from non-volatile storage to hardwareregisters) when the software program is referred to as “implemented in acomputer-readable medium.” A processor is considered to be “configuredto execute a program” when at least one value associated with theprogram is stored in a register readable by the processor.

In operation, the computer system 2000 can be controlled by operatingsystem software that includes a file management system, such as a diskoperating system. One example of operating system software withassociated file management system software is the family of operatingsystems known as Windows® from Microsoft Corporation of Redmond, Wash.,and their associated file management systems. Another example ofoperating system software with its associated file management systemsoftware is the Linux operating system and its associated filemanagement system. The file management system is typically stored in thenon-volatile memory and/or drive unit and causes the processor toexecute the various acts required by the operating system to input andoutput data and to store data in the memory, including storing files onthe non-volatile memory and/or drive unit.

Some portions of the detailed description may be presented in terms ofalgorithms and symbolic representations of operations on data bitswithin a computer memory. These algorithmic descriptions andrepresentations are the means used by those skilled in the dataprocessing arts to most effectively convey the substance of their workto others skilled in the art. An algorithm is, here and generally,conceived to be a self-consistent sequence of operations leading to adesired result. The operations are those requiring physicalmanipulations of physical quantities. Usually, though not necessarily,these quantities take the form of electrical or magnetic signals capableof being stored, transferred, combined, compared, and otherwisemanipulated. It has proven convenient at times, principally for reasonsof common usage, to refer to these signals as bits, values, elements,symbols, characters, terms, numbers, or the like.

The algorithms and displays presented herein are not inherently relatedto any particular computer or other apparatus. Various general purposesystems may be used with programs in accordance with the teachingsherein, or it may prove convenient to construct more specializedapparatus to perform the methods of some embodiments. The requiredstructure for a variety of these systems will appear from thedescription below. In addition, the techniques are not described withreference to any particular programming language, and variousembodiments may, thus, be implemented using a variety of programminglanguages.

In alternative embodiments, the machine operates as a standalone deviceor may be connected (e.g., networked) to other machines. In a networkeddeployment, the machine may operate in the capacity of a server or aclient machine in a client-server network environment or as a peermachine in a peer-to-peer (or distributed) network environment.

The machine may be a server computer, a client computer, a personalcomputer (PC), a tablet PC, a laptop computer, a set-top box (STB), apersonal digital assistant (PDA), a cellular telephone, an iPhone, aBlackberry, a processor, a telephone, a web appliance, a network router,switch or bridge, or any machine capable of executing a set ofinstructions (sequential or otherwise) that specify actions to be takenby that machine.

While the machine-readable medium or machine-readable storage medium isshown in an exemplary embodiment to be a single medium, the term“machine-readable medium” and “machine-readable storage medium” shouldbe taken to include a single medium or multiple media (e.g., acentralized or distributed database, and/or associated caches andservers) that store the one or more sets of instructions. The term“machine-readable medium” and “machine-readable storage medium” shallalso be taken to include any medium that is capable of storing, encodingor carrying a set of instructions for execution by the machine and thatcause the machine to perform any one or more of the methodologies of thepresently disclosed technique and innovation.

In general, the routines executed to implement the embodiments of thedisclosure may be implemented as part of an operating system or aspecific application, component, program, object, module or sequence ofinstructions referred to as “computer programs.” The computer programstypically comprise one or more instructions set at various times invarious memory and storage devices in a computer, and when read andexecuted by one or more processing units or processors in a computer,cause the computer to perform operations to execute elements involvingthe various aspects of the disclosure.

Moreover, while embodiments have been described in the context of fullyfunctioning computers and computer systems, those skilled in the artwill appreciate that the various embodiments are capable of beingdistributed as a program product in a variety of forms, and that thedisclosure applies equally regardless of the particular type of machineor computer-readable media used to actually effect the distribution

While this invention has been described in terms of several embodiments,there are alterations, modifications, permutations, and substituteequivalents, which fall within the scope of this invention. Althoughsub-section titles have been provided to aid in the description of theinvention, these titles are merely illustrative and are not intended tolimit the scope of the present invention. It should also be noted thatthere are many alternative ways of implementing the methods andapparatuses of the present invention. It is therefore intended that thefollowing appended claims be interpreted as including all suchalterations, modifications, permutations, and substitute equivalents asfall within the true spirit and scope of the present invention.

What is claimed is:
 1. A method for scoring a set of promotionscomprising: receiving, by one or more processors, a set of trainingoffers comprising a plurality of variables, each variable having one ofa set of values to form a combination of variable values; converting, bythe one or more processors, the combination of variable values for eachof the training offers into a vector; generating, by the one or moreprocessors, pairings of the training offers such that all combinationsof training offer pairs is represented; subtracting, by the one or moreprocessors, the vector of one training offer in each pair from the othervector of the other training offer of the pair to generate a pairvector; collecting, by the one or more processors, success metrics foreach of the training offers from a retailer's point of sales system, acomputerized application, and from consumer mobile devices; subtracting,by the one or more processors, the success metrics of the one trainingoffer in each pair from the other success metrics of the other trainingoffer of the pair to generate a raw score; generating, by the one ormore processors, a normalized score for each of the pairings using theraw score and the pair vector; determining, by the one or moreprocessors, that the set of training offers consists of offersoriginating from a single client; responsive to the determination thatset of training offers consists of offers originating from a singleclient, generating, by the one or more processors, a decision tree modelby machine learning and using the normalized scores, wherein the one ormore processors are configured to generate one of two model typesdependent upon at least one of the amount of training offers in the setof training offers or the number of clients the offers in the set oftraining offers originate from, wherein the two model types are adecision tree and a neural network; and applying, by the one or moreprocessors, the model to a set of new offers to generate a score foreach new offer.
 2. The method of claim 1, wherein the one or moreprocessors are configured to generate a neural network when the set oftraining offers consists of offers originating from multiple clients. 3.The method of claim 1, wherein the success metrics include a klip rate,redemption rate, viewing rate, saving rate, sharing rate and impressionmeasure.
 4. The method of claim 3, wherein the success metrics areconsolidated as a weighted average.
 5. The method of claim 1, whereinapplying the model comprises utilizing linear regression.
 6. The methodof claim 5, wherein applying the model comprises generating an estimateand a t-value.
 7. The method of claim 6, wherein the generated score isthe estimate.
 8. The method of claim 6, wherein the generated score isthe t-value.
 9. The method of claim 1 further comprising ranking, by theone or more processors, the new offers by their scores; and generating aset of top ranked offers, wherein the set of top ranked new offers isbetween 4 and 10 offers.
 10. The method of claim 1 further comprisingranking, by the one or more processors, the new offers by their scores;and generating a set of top ranked offers, wherein the set of top rankednew offers is the highest 10-30% of the set of new offers.
 11. A methodfor scoring a set of promotions comprising: receiving, by one or moreprocessors, a set of training offers comprising a plurality ofvariables; collecting, by the one or more processors, success metricsfor each training offer; subtracting, by the one or more processors, thesuccess metrics of each training offer from the success metrics of eachother training offer to generate raw scores; pairing, by the one or moreprocessors, the training offer to generate pairings; generating, by theone or more processors, a normalized score for each of the pairingsusing the raw scores; determining, by the one or more processors, thatthe set of training offers consists of offers originating from multipleclients; responsive to the determination that set of training offersconsists of offers originating from multiple clients, generating, by theone or more processors, a neural network model by machine learning andusing the normalized scores, wherein the one or more processors areconfigured to generate one of two model types dependent upon at leastone of the amount of training offers in the set of training offers orthe number of clients the offers in the set of training offers originatefrom, wherein the two model types are a decision tree and a neuralnetwork; and applying, by the one or more processors, the model to a setof new offers to generate a predicted score for each new offer.
 12. Themethod of claim 11, wherein the one or more processors are configured togenerate a decision tree when the set of training offers consists ofoffers originating from a single client.
 13. The method of claim 11,wherein the success metrics include a klip rate, redemption rate,viewing rate, saving rate, sharing rate and impression measure.
 14. Themethod of claim 11, wherein the success metrics are consolidated as aweighted average.
 15. The method of claim 11, wherein applying the modelcomprises utilizing a linear regression.
 16. The method of claim 14,wherein applying the model comprises generating an estimate and at-value.
 17. The method of claim 15, wherein the predicted score is theestimate.
 18. The method of claim 15, wherein the predicted score is thet-value.
 19. The method of claim 11, further comprising ranking, by theone or more processors, the new offers by their scores; and generating aset of top ranked offers, wherein the set of top ranked new offers isbetween 4 and 10 offers.
 20. The method of claim 11, further comprisingranking the new offers by their scores; and generating a set of topranked offers, wherein the set of top ranked new offers is the highest10-30% of the set of new offers.